Assessment of two statistical approaches for variance genome-wide association studies in plants
Assessment of two statistical approaches for variance genome-wide association studies in plants
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DOI：
10.1038/s41437-022-00541-1
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发表时间：
2022-05-10
期刊：
影响因子：
3.8
通讯作者：
Lipka，Alexander E.
中科院分区：
文献类型：
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作者：
Murphy，Matthew D.;Fernandes，Samuel B.;Lipka，Alexander E.
由于气候变化引起的不可预测环境的丰富，控制农学重要性状变异的基因组位点变得越来越重要。在关联研究中识别这种变异控制位点的能力将对未来的育种工作至关重要。在变异全基因组关联研究（vGWAS）范式中已经使用的两种统计方法是Brown-Forsythe检验（BFT）和双广义线性模型（DGLM）。为了确保尽可能有效地部署这些方法，研究影响其识别方差控制位点能力的因素至关重要。我们利用玉米（Zea maysL.）和拟南芥的全基因组标记数据来模拟上位性、环境相互作用基因型和变异数量性状核苷酸（vQTNs）控制的性状。然后，我们量化了所有模拟性状的BFT和DGLM的真阳性和假阳性检出率。我们还使用BFT和DGLM对玉米多样性面板的植株高度进行了vGWAS。在考虑的最大样本量（N= 2815）下观察到的真阳性检出率表明，在足够大的样本量下，这两种vGWAS方法都能够识别上位性和GxE。我们还注意到，当样本量为n = 500时，DGLM在由vqtn控制的模拟性状上的表现明显优于BFT。尽管我们得出结论，vGWAS方法仍有某些方面需要进一步改进，但本研究表明，BFT和DGLM能够在当前最先进的植物或农艺数据集中识别方差控制位点。
Genomic loci that control the variance of agronomically important traits are increasingly important due to the profusion of unpredictable environments arising from climate change. The ability to identify such variance-controlling loci in association studies will be critical for future breeding efforts. Two statistical approaches that have already been used in the variance genome-wide association study (vGWAS) paradigm are the Brown–Forsythe test (BFT) and the double generalized linear model (DGLM). To ensure that these approaches are deployed as effectively as possible, it is critical to study the factors that influence their ability to identify variance-controlling loci. We used genome-wide marker data in maize (Zea maysL.) andArabidopsis thalianato simulate traits controlled by epistasis, genotype by environment (GxE) interactions, and variance quantitative trait nucleotides (vQTNs). We then quantified true and false positive detection rates of the BFT and DGLM across all simulated traits. We also conducted a vGWAS using both the BFT and DGLM on plant height in a maize diversity panel. The observed true positive detection rates at the maximum sample size considered (N= 2815) suggest that both of these vGWAS approaches are capable of identifying epistasis and GxE for sufficiently large sample sizes. We also noted that the DGLM decisively outperformed the BFT for simulated traits controlled by vQTNs at sample sizes ofN= 500. Although we conclude that there are still certain aspects of vGWAS approaches that need further refinement, this study suggests that the BFT and DGLM are capable of identifying variance-controlling loci in current state-of-the-art plant or agronomic data sets.